Orange: Linear Regression - Revision history

Onnowpurbo: /* Output */

2020-04-06T02:11:12Z

Output

← Older revision		Revision as of 02:11, 6 April 2020
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	Widget Linear Regression membangun learner/predictor yang akan learn fungsi linear function dari input data-nya. Model dapat mengidentifikasi hubungan antara predictor xi dan response variable y. Di samping itu, parameter regularisasi Lasso and Ridge dapat di spesifikasikan. Lasso regression meminimalisasi penalized version dari least squares loss function dengan L1-norm penalty sedangkan regularisai Ridge dengan L2-norm penalty.		Widget Linear Regression membangun learner/predictor yang akan learn fungsi linear function dari input data-nya. Model dapat mengidentifikasi hubungan antara predictor xi dan response variable y. Di samping itu, parameter regularisasi Lasso and Ridge dapat di spesifikasikan. Lasso regression meminimalisasi penalized version dari least squares loss function dengan L1-norm penalty sedangkan regularisai Ridge dengan L2-norm penalty.

	Widget Linear ~~regression~~ hanya dapat berfungsi / bekerja pada task ~~regression~~.		Widget Linear Regression hanya dapat berfungsi / bekerja pada regression task.

	[[File:LinearRegression-stamped.png\|center\|200px\|thumb]]		[[File:LinearRegression-stamped.png\|center\|200px\|thumb]]

Onnowpurbo at 10:23, 5 April 2020

2020-04-05T10:23:24Z

← Older revision		Revision as of 10:23, 5 April 2020
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	~~Sebuah~~ algoritma linear regression dengan regularisasi optional L1 (LASSO), L2 (ridge) atau L1L2 (elastic net).		Widget Linear Regression mengimplementasikan sebuah algoritma linear regression dengan regularisasi optional L1 (LASSO), L2 (ridge) atau L1L2 (elastic net).

	==Input==		==Input==
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	Widget Linear Regression membangun learner/predictor yang akan learn fungsi linear function dari input data-nya. Model dapat mengidentifikasi hubungan antara predictor xi dan response variable y. Di samping itu, parameter regularisasi Lasso and Ridge dapat di spesifikasikan. Lasso regression meminimalisasi penalized version dari least squares loss function dengan L1-norm penalty sedangkan regularisai Ridge dengan L2-norm penalty.		Widget Linear Regression membangun learner/predictor yang akan learn fungsi linear function dari input data-nya. Model dapat mengidentifikasi hubungan antara predictor xi dan response variable y. Di samping itu, parameter regularisasi Lasso and Ridge dapat di spesifikasikan. Lasso regression meminimalisasi penalized version dari least squares loss function dengan L1-norm penalty sedangkan regularisai Ridge dengan L2-norm penalty.

	Linear regression hanya dapat berfungsi / bekerja pada task regression.		Widget Linear regression hanya dapat berfungsi / bekerja pada task regression.

	[[File:LinearRegression-stamped.png\|center\|200px\|thumb]]		[[File:LinearRegression-stamped.png\|center\|200px\|thumb]]

Onnowpurbo: /* Contoh */

2020-04-05T10:15:16Z

Contoh

← Older revision		Revision as of 10:15, 5 April 2020
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	==Contoh==		==Contoh==

	Di bawah ini adalah workflow sederhana dengan housing dataset. Kita men-train Linear Regression dan Random Forest dan mengevaluasi performance-nya di Test & Score.		Di bawah ini adalah workflow sederhana dengan housing dataset. Kita men-train widget Linear Regression dan widget Random Forest dan mengevaluasi performance-nya di widget Test & Score.

	~~[[File:LinearRegression-regression.png\|center\|200px\|thumb]]~~


			[[File:LinearRegression-regression.png\|center\|600px\|thumb]]

	==Referensi==		==Referensi==

Onnowpurbo at 07:53, 6 March 2020

2020-03-06T07:53:03Z

← Older revision		Revision as of 07:53, 6 March 2020
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	A linear regression ~~algorithm with~~ optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) ~~regularization~~.		Sebuah algoritma linear regression dengan regularisasi optional L1 (LASSO), L2 (ridge) atau L1L2 (elastic net).

	==Input==		==Input==
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	Coefficients: linear regression coefficients		Coefficients: linear regression coefficients

	~~The~~ Linear Regression ~~widget constructs a~~ learner/predictor ~~that learns a~~ linear function ~~from its~~ input data. ~~The model can identify the relationship between a~~ predictor xi ~~and the~~ response variable y. ~~Additionally~~, Lasso and Ridge ~~regularization parameters can be specified~~. Lasso regression ~~minimizes a~~ penalized version ~~of the~~ least squares loss function ~~with~~ L1-norm penalty ~~and~~ Ridge ~~regularization with~~ L2-norm penalty.		Widget Linear Regression membangun learner/predictor yang akan learn fungsi linear function dari input data-nya. Model dapat mengidentifikasi hubungan antara predictor xi dan response variable y. Di samping itu, parameter regularisasi Lasso and Ridge dapat di spesifikasikan. Lasso regression meminimalisasi penalized version dari least squares loss function dengan L1-norm penalty sedangkan regularisai Ridge dengan L2-norm penalty.

	Linear regression ~~works only on~~ regression ~~tasks~~.		Linear regression hanya dapat berfungsi / bekerja pada task regression.

	[[File:LinearRegression-stamped.png\|center\|200px\|thumb]]		[[File:LinearRegression-stamped.png\|center\|200px\|thumb]]
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	==Contoh==		==Contoh==

	~~Below, is a simple~~ workflow ~~with~~ housing dataset. ~~We trained~~ Linear Regression ~~and~~ Random Forest ~~and evaluated their~~ performance in Test & Score.		Di bawah ini adalah workflow sederhana dengan housing dataset. Kita men-train Linear Regression dan Random Forest dan mengevaluasi performance-nya di Test & Score.

	[[File:LinearRegression-regression.png\|center\|200px\|thumb]]		[[File:LinearRegression-regression.png\|center\|200px\|thumb]]

Onnowpurbo at 03:56, 28 January 2020

2020-01-28T03:56:39Z

← Older revision		Revision as of 03:56, 28 January 2020
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	A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization.		A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset
	Preprocessor: preprocessing method(s)		Preprocessor: preprocessing method(s)

	~~Outputs~~		==Output==

	Learner: linear regression learning algorithm		Learner: linear regression learning algorithm
	Model: trained model		Model: trained model
	Coefficients: linear regression coefficients		Coefficients: linear regression coefficients

	The Linear Regression widget constructs a learner/predictor that learns a linear function from its input data. The model can identify the relationship between a predictor xi and the response variable y. Additionally, Lasso and Ridge regularization parameters can be specified. Lasso regression minimizes a penalized version of the least squares loss function with L1-norm penalty and Ridge regularization with L2-norm penalty.		The Linear Regression widget constructs a learner/predictor that learns a linear function from its input data. The model can identify the relationship between a predictor xi and the response variable y. Additionally, Lasso and Ridge regularization parameters can be specified. Lasso regression minimizes a penalized version of the least squares loss function with L1-norm penalty and Ridge regularization with L2-norm penalty.
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	[[File:LinearRegression-stamped.png\|center\|200px\|thumb]]		[[File:LinearRegression-stamped.png\|center\|200px\|thumb]]

	The learner/predictor name		* The learner/predictor name
	Choose a model to train:		* Choose a model to train:
	no regularization		** no regularization
	a Ridge regularization (L2-norm penalty)		** a Ridge regularization (L2-norm penalty)
	a Lasso bound (L1-norm penalty)		** a Lasso bound (L1-norm penalty)
	an Elastic net regularization		** an Elastic net regularization
	Produce a report.
	Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.		* Produce a report.
			* Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.

	==Contoh==		==Contoh==

Onnowpurbo at 02:44, 23 January 2020

2020-01-23T02:44:31Z

← Older revision		Revision as of 02:44, 23 January 2020
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	Data: input dataset		Data: input dataset

	Preprocessor: preprocessing method(s)		Preprocessor: preprocessing method(s)

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	Learner: linear regression learning algorithm		Learner: linear regression learning algorithm

	Model: trained model		Model: trained model

	Coefficients: linear regression coefficients		Coefficients: linear regression coefficients

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	Linear regression works only on regression tasks.		Linear regression works only on regression tasks.

	~~../../_images/~~LinearRegression-stamped.png		[[File:LinearRegression-stamped.png\|center\|200px\|thumb]]

	The learner/predictor name		The learner/predictor name

	Choose a model to train:		Choose a model to train:

	no regularization		no regularization

	a Ridge regularization (L2-norm penalty)		a Ridge regularization (L2-norm penalty)

	a Lasso bound (L1-norm penalty)		a Lasso bound (L1-norm penalty)

	an Elastic net regularization		an Elastic net regularization

	Produce a report.		Produce a report.

	Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.		Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.

	~~Example~~		==Contoh==

	Below, is a simple workflow with housing dataset. We trained Linear Regression and Random Forest and evaluated their performance in Test & Score.		Below, is a simple workflow with housing dataset. We trained Linear Regression and Random Forest and evaluated their performance in Test & Score.

	~~../../_images/~~LinearRegression-regression.png		[[File:LinearRegression-regression.png\|center\|200px\|thumb]]

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/linearregression.html A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic..."

2020-01-12T13:49:56Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/linearregression.html A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic..."

New page

Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/linearregression.html

A linear regression algorithm with optional L1 (LASSO), L2 (ridge) or L1L2 (elastic net) regularization.

Inputs

Data: input dataset

Preprocessor: preprocessing method(s)

Outputs

Learner: linear regression learning algorithm

Model: trained model

Coefficients: linear regression coefficients

The Linear Regression widget constructs a learner/predictor that learns a linear function from its input data. The model can identify the relationship between a predictor xi and the response variable y. Additionally, Lasso and Ridge regularization parameters can be specified. Lasso regression minimizes a penalized version of the least squares loss function with L1-norm penalty and Ridge regularization with L2-norm penalty.

Linear regression works only on regression tasks.

../../_images/LinearRegression-stamped.png

The learner/predictor name

Choose a model to train:

no regularization

a Ridge regularization (L2-norm penalty)

a Lasso bound (L1-norm penalty)

an Elastic net regularization

Produce a report.

Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.

Example

Below, is a simple workflow with housing dataset. We trained Linear Regression and Random Forest and evaluated their performance in Test & Score.

../../_images/LinearRegression-regression.png

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/model/linearregression.html

==Pranala Menarik==

* [[Orange]]